Social learning under randomized collaborations
2022 IEEE International Symposium on Information Theory (ISIT), 2022•ieeexplore.ieee.org
We study a social learning scheme where at every time instant, each agent chooses to
receive information from one of its neighbors at random. We show that under this sparser
communication scheme, the agents learn the truth eventually and the asymptotic
convergence rate remains the same as the standard algorithms, which use more
communication resources. We also derive large deviation estimates of the log-belief ratios
for a special case where each agent replaces its belief with that of the chosen neighbor.
receive information from one of its neighbors at random. We show that under this sparser
communication scheme, the agents learn the truth eventually and the asymptotic
convergence rate remains the same as the standard algorithms, which use more
communication resources. We also derive large deviation estimates of the log-belief ratios
for a special case where each agent replaces its belief with that of the chosen neighbor.
We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms, which use more communication resources. We also derive large deviation estimates of the log-belief ratios for a special case where each agent replaces its belief with that of the chosen neighbor.
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